Full profile of 10,000 people in the US - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!
There are additionally 258+ Million US people profiles available, visit the LinkDB product page here.
Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.
The global number of LinkedIn users in was forecast to continuously increase between 2024 and 2028 by in total ***** million users (+**** percent). After the sixth consecutive increasing year, the LinkedIn user base is estimated to reach ****** million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Asia and South America.
Full profile of 10,000 people in Brazil - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!
There are additionally millions more Brazilian people profiles available, visit the LinkDB product page here.
Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.
https://cdla.io/permissive-1-0/https://cdla.io/permissive-1-0/
The dataset contains information on 30,000+ job postings collected from LinkedIn till the year 2023 which provides a rich source of information on job postings on LinkedIn, with concise information on the job title, company, location, and other key attributes of each posting. This data can be used to gain insights into employment trends and dynamics, identify key skills and experiences that are in high demand, and optimize job postings to attract the right candidates.
Taxonomy of the Dataset
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13623947%2F85fde0e9bcd9e6532b63e65ca1e5b58a%2FWhatsApp%20Image%202024-02-27%20at%2012.12.59.jpeg?generation=1709016197299811&alt=media" alt="">
Download verified LinkedIn profiles of startup founders worldwide. Includes work emails, roles, funding rounds, and firmographics. Ideal for VCs, SaaS vendors, and growth platforms. Best Price Guarantee.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
This data is collected from 24th Aug 2020 to 29th Aug 2020 from LinkedIn and Facebook, with the intention of analyzing the best social groups to join for Data Scientists. The data is collected for the group with more than 50K members.
Survey: This sheet contains the basic information about the groups, number of members, Name and summary and calculated scores from other sheets. Sheets 1-15: are the detailed posts information in individual groups. Groups are given a serial number from 1-15, 1 contains the highest number of members and 15 contains the lowest number of members. Individual group content detail is in individual sheet
I have requested access in these groups from over a month. Once I got the access to all the groups needed, I have started collecting data manually. LinkedIn Profile: https://www.linkedin.com/in/shrashti-singhal-1869166b/
My inspiration to collect this data was to help myself and others decide for ourselves(Data Scientists) where to put our energies to network best.
Please feel free to explore, download the data. Text me for any discussions on inconsistencies, scoring, data collection techniques, missing fields or any improvement suggestions. Please also feel free to expand the data.
https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified
The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s
The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs
We have made it as simple as possible to collect data from websites
Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.
Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.
Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.
Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.
Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.
Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.
Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.
Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.
Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.
Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.
Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.
Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.
Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.
Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.
LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.
Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.
Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.
Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.
Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.
Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.
Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.
Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.
Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.
Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Fala turma, Tudo bom?
Esse dataset contém informações da CVM, Fundamentus e Yahoo Finance. E faz parte do nosso projeto anual da faculdade de engenharia da computação de criar um sistema SAD. Como já estamos consumindo esses dados e eles são públicos, e estamos utilizando o kaggle como nosso motor de ingestão de dados na AWS, não nos custaria nada disponibilizar esses arquivos aqui para a galera do Kaggle. Vou dar mais detalhes a baixo.
Link do nosso site: http://theras.online
Link da CVM: https://dados.cvm.gov.br
Link da Fundamentus: https://www.fundamentus.com.br
Nos ajude com um pix, qualquer valor já nos ajudaria!
PIX: 63acaea3-7669-4380-9784-68148e421375 (chave aleatória) | Marcus Vinicius Souza Rodrigues
QrCode (PIX):
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2Fd1b94b8e43cd7d2d76db91d55d3bbef8%2Fimagem_2023-09-10_164528557.png?generation=1694375128522564&alt=media" alt="">
Qualquer dúvida pode me chamar no Whatsapp: (11)94937-0306
Meu Linkedin: https://www.linkedin.com/in/marcus-vinicius-贺辰淼/
Turma, os arquivos estão em formato parquet visto que estamos utilizando o PySpark para fazer o ETL, e estamos buscando ter o máximo de aproveitamento das ferramentas. E como esse formato é extremamente rápido, resolvemos adotar no nosso projeto.
Infelizmente não vamos conseguir disponibilizar o notebook que utilizamos para pegar essas informações, visto que contém as chaves da nossa conta AWS.
Como ainda estamos desenvolvendo nosso projeto, vamos estar colocando outras informações aqui nesse dataset, como por exemplo o resultado do nosso modelo de Machine Learning.
Eu vou mostrar para vocês como está nosso Pipeline de ingestão de dados:
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2F401e2f3a246ed6d6aa88701b5ff4a92d%2F1.png?generation=1694148808596951&alt=media" alt="">
A gente focou em criar um modelo funcional e montar um front end com o flutter, para termos um 'entregável'. E por isso a necessidade de criar esse pipeline, visto que estamos fazendo de forma manual. Falta nos modernizar um outro pipeline que é voltado em fazer as predições e clusterizações, que vai consumir os dados desse pipeline de ingestão, e nesse processo vamos melhorar os modelos que temos atualmente. Temos melhorias para o front end, que tem várias features no nosso backlog.
A gente fez uma modelagem de dados também, de todas as tabelas disponibilizadas. Com objetivo de padronizar os campos e trazer qualidade e domínio dos dados.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2F9872acbd50ba71b03f61b5784602176f%2F7.PNG?generation=1694149602063394&alt=media" alt="">
Olha como ficou as tabelas la no AWS Redshift e AWS S3:
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2F94bda97bc96decac203011e3735cc907%2F5.PNG?generation=1694150013623768&alt=media" alt="">
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2F987e407f01505f63b657756416eacfcd%2F4.PNG?generation=1694150032840401&alt=media" alt="">
Olha uma fotinha do notebook que roda esse processo aqui no Kaggle:
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16582479%2F9693010523125a055de0ef9da4fff921%2F2.PNG?generation=1694150090892007&alt=media" alt="">
EXIOBASE 3: For best in class environmental-economic accounting data. Get insight into global supply-chains and the environmental impacts of consumption.
EXIOBASE 3 provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to 2020 (plus now-casted tables for 2021 and 2022) for 44 countries (27 EU member plus 17 major economies) and five rest of the world regions.
EXIOBASE is maintained by the EXIOBASE consortium, with XIO Sustainability Analytics now working on providing annual updates to the core economic, energy and emission tables. We welcome any collaborative efforts to further improve the database.
Updates are now being produced annually, and more updated data may be available in beta-mode, get in contact if interested. At time of publication of v3.9.4, a version 3.10 with updates to 2022 and nowcasts to 2024 is in beta.
A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3. This includes the article by Stadler et al. (2018) describing the compilation of EXIOBASE 3.
To stay updated on database improvements, relevant EXIOBASE studies, and ongoing work, join the EXIOBASE group on LinkedIn.
Licenses
Please ensure that you have understood the license conditions before use. Note that these conditions are significantly different to the license conditions of earlier versions, such as v3.8.
Non-commercial, academic useEXIOBASE v3.9 is released under a customized derivative of the CC-BY-SA-NC license, incorporating additional definitions as outlined in the license file.
Commercial useCommercial licenses, which allow for use for any case not covered in the non-commercial license are under development. For license enquiries or help in use of EXIOBASE data for spend-based emission factors, or other applications, please send an email.
The funding to be accumulated through licenses and support will be used to fund further updates of the database.
Now-casting
The core EXIOBASE 3.9 model is based on supply and use tables up to 2020. However, the time-series is expanded (i.e., now-casted) until 2022 using global trade data and macroeconomic data (IMF), as well as environmental data when available. Caution should be made when using now-casted data, especially due to the impact of the COVID pandemic not being adequately captured in the now-casting. It is recommended to use 2020 data from v3.9.4 as the latest available year for most analysis.
Processing the database
For a general introduction to environmentally extended input-output modelling, we refer to:
UN Handbook on Supply and Use Tables and Input Output-Tables with Extensions and Applications
Input-Output Analysis by Miller & Blair
The database is too large to handle in a standard spreadsheet software (e.g., Excel), and we recommend using programming languages such as Python, R, or Matlab. The open-source python package PyMRIO can be used to download and parse the database directly from Zenodo and do input-output analysis.
If you are interested in learning more about EXIOBASE or input-output modelling in general (including practical use of PyMRIO, how to develop custom models), please reach out.
Earlier versions and documentation
Some previous versions (3.7, 3.8) are also available on Zenodo. The even earlier public releases of the data (EXIOBASE v3.3 and v3.4) are available on request. We recommend, however, using the latest version due to significant updates of the economic data as well as major differences in water and land use accounts.
The first documentation of EXIOBASE 3 was done via deliverables of the DESIRE project - these can now be accessed here.
The country disaggregated version, EXIOBASE 3rx, is available on Zenodo. It is no longer continued, but including more regions in the EXIOBASE classification is ongoing work. Reach out to exiobase-support@googlegroups.com, if interested in collaboration on integrating specific countries.
Future Updates and Announcements
Updates are now being produced annually, and a beta version of 3.10 is already under development, extending most data to 2022. To stay updated, join the EXIOBASE group on LinkedIn and/or reach out to exiobase-support@googlegroups.com.
Success.ai’s B2B Contact Data and Ecommerce Merchant Data for Retail Executives Worldwide provides a powerful solution for businesses looking to connect with decision-makers in the retail industry. With access to over 170 million verified professional profiles, this dataset includes the contact information you need to build relationships with retail executives globally. Whether you're targeting C-level leaders, operations managers, or marketing heads, Success.ai’s data ensures precise and impactful outreach.
Why Choose Success.ai’s Retail Executives Data?
Data is AI-validated to ensure 99% accuracy for all your outreach efforts.
Global Reach Across Retail Sectors:
Includes executives from sectors like e-commerce, fashion, grocery, electronics, and luxury goods.
Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.
Continuously Updated Datasets:
Real-time updates ensure accurate and current information about retail professionals in leadership roles.
Compliance You Can Trust:
Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring ethical data use.
Data Highlights: - 170M+ Verified Professional Profiles: Drawn from diverse industries, including retail. - 50M Work Emails: AI-validated for high accuracy and reliability. - 30M Company Profiles: Detailed insights to support targeted campaigns. - 700M Global Professional Profiles: Enriched datasets to meet broad business objectives.
Key Features of the Dataset: - Retail Decision-Maker Profiles: Includes profiles of CEOs, CFOs, CMOs, buyers, and merchandising directors. - Advanced Filters for Targeting: Refine your search by location, role, revenue, or retail category for optimal results. - AI-Driven Insights: Enriches profiles with valuable data to personalize and enhance your outreach.
Strategic Use Cases:
Build relationships with executives who influence major purchasing decisions.
Recruitment for Retail Talent:
Identify top retail professionals to fill critical leadership roles.
Connect with candidates using updated and accurate contact information.
Targeted Marketing Campaigns:
Craft highly personalized campaigns aimed at retail decision-makers.
Leverage detailed contact data for better conversion rates.
Retail Technology Solutions:
Present technology solutions like POS systems, inventory tools, or e-commerce platforms to relevant retail executives.
Build connections with leaders looking to innovate their businesses.
Why Choose Success.ai?
APIs for Enhanced Functionality
Unlock opportunities with B2B Contact Data for Retail Executives Worldwide from Success.ai. This dataset includes verified emails, phone numbers, and decision-maker profiles for leaders in the retail industry.
With continuously updated data and a Best Price Guarantee, Success.ai ensures you have everything you need to connect with global retail executives effectively. Contact us now to elevate your business with precise and reliable data!
No one beats us on price. Period.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
This is the dataset for the Axisymmetric body (0.16m diameter, elliptical nose cone, l/d = 5), collated by Max Varney (https://www.linkedin.com/in/max-varney/) on 2020/06/15. More information for the geometry and analysis using dataset #1 can be found in the paper "Three dimensional structure of the unsteady wake of an axisymmetric body", Physics of Fluids 31, 025113 (2019); https://doi.org/10.1063/1.5078379. The paper is also available on the Loughbrough University research repository at: https://hdl.handle.net/2134/37058Data was collected in the Large Wind Tunnel at Loughborough University, a 2.5m^2, closed working section, fixed ground open return tunnel. Details of the tunnel can be found in: https://hdl.handle.net/2134/6674The CAD geometry for the wind tunnel and the axisymmetric body with its mounting are included in the dataset as ASCII .stl files, with the units in meters. Note: at the start of every experiment the yaw and pitch of the model was incrementally adjusted to produce a symmetric base pressure.All data is presented in SI units and all measurements are from the origin of the model (on the base of the model, at the centre of the base) with x positive downstream and z positive up, using a right hand rule to find positive y.There are two datasets. DATASET #1 - 30m/s (Re_d=3.2x10^5)Contains: Tomographic Particle Image Velocimetry and Base PressuresDATASET #2 - 40m/s (Re_d=4.3x10^5)Contains: Planar (Y=0m, Z=0m and Z=0.04m) and Stereo (X=0.06m, X=0.12m, X=0.18m and X=0.24m) Particle Image Velocimetry, Base Pressures and Forces
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Overview
3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.
Our corresponding paper (published at ITSC 2022) is available here.
Further, we have applied 3DHD CityScenes to map deviation detection here.
Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:
The DevKit is available here:
https://github.com/volkswagen/3DHD_devkit.
The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.
When using our dataset, you are welcome to cite:
@INPROCEEDINGS{9921866,
author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and
Fingscheidt, Tim},
booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)},
title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds},
year={2022},
pages={627-634}}
Acknowledgements
We thank the following interns for their exceptional contributions to our work.
The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.
The Dataset
After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.
1. Dataset
This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.
During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.
To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.
import json
json_path = r"E:\3DHD_CityScenes\Dataset\train.json"
with open(json_path) as jf:
data = json.load(jf)
print(data)
2. HD_Map
Map items are stored as lists of items in JSON format. In particular, we provide:
3. HD_Map_MetaData
Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.
Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.
4. HD_PointCloud_Tiles
The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.
After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.
import numpy as np
import pptk
file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin"
pc_dict = {}
key_list = ['x', 'y', 'z', 'intensity', 'is_ground']
type_list = ['
5. Trajectories
We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.
These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.
- OP1 – OP5 cover the majority of the map with 5 trajectories.
- RH1 – RH10 cover the majority of the map with 10 trajectories.
Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here.
https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval
Graph and download economic data for Software Development Job Postings on Indeed in the United States (IHLIDXUSTPSOFTDEVE) from 2020-02-01 to 2025-10-10 about software, jobs, and USA.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.
The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.
The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.
This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.
This dataset is aslo available as Google Earth Engine application. For more details check:
> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf
If you find this work useful, please consider citing it as Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.
Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil
To check other work, please check:
My Webpage & Google Scholar
The number of LinkedIn users in Africa was forecast to continuously increase between 2024 and 2028 by in total 37 million users (+68.13 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 91.29 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like South America and Caribbean.
The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of LinkedIn users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
The number of LinkedIn users in Nigeria was forecast to continuously increase between 2024 and 2028 by in total 2.4 million users (+25.89 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 11.64 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Ghana and Senegal.
The number of LinkedIn users in Ghana was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+10.6 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 3.14 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Ivory Coast and Nigeria.
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